Credit Risk Forecasting

Machine learning models that improved default prediction accuracy by 15% across partner portfolios.

Problem

Lending teams needed actionable forecasts that could flag at-risk accounts before delinquency while handling millions of historical records sourced from SQL data warehouses and flat files. Existing spreadsheet models required heavy manual upkeep and routinely lagged behind real performance.

Approach

• Audited and engineered features from 40+ raw attributes, adding bureau scores, behavioral ratios, and macroeconomic markers.
• Benchmarked gradient boosting, stacked ensemble, and neural architectures; selected a blended TensorFlow + XGBoost pipeline for stability.
• Containerized training and scoring flows with Git-driven review gates plus automated evaluation notebooks.

Impact

The production pipeline refreshed predictions daily and surfaced early-warning dashboards for credit analysts. Default detection improved 15%, loss provisioning accuracy tightened, and loan officers were able to intervene sooner with personalized retention plans.